Accident prediction information and statistical/analytical information on accidents are useful in preventing vehicle accidents. Such information are provided to, for example, drivers, road administrators who are responsible for road safety design or for considering improvement measures, police who inspect traffic accidents and organize traffic safety campaigns, accident appraisers and insurers that conduct accident analyses, and so forth.
One known method for collecting such information is the drive recorder, for example. A drive recorder records images/video and sensor information of the few seconds before and after a sudden braking event detected by a vehicle-mounted sensor. The information recorded on the drive recorder is visualized, presented to the driver by a business operator that manages the vehicle, and thus utilized to raise awareness regarding traffic safety. The “Hiyari-Hatto Database” compiled by the Society of Automotive Engineers of Japan, which is a database comprised of image/videos and sensor information from drive recorders, enables causal analyses of accidents based on large volumes of hiyari-hatto data, and is used by auto manufacturers in developing traffic safety assistance apparatuses, and/or the like. The term “hiyari-hatto” refers to a state where, although a collision did not take place, one was close to happening.
Although such drive recorders are gradually becoming common place on business vehicles, e.g., taxis, buses, etc., it is unrealistic to expect drive recorders to be mounted on all vehicles on public roads, including ordinary vehicles. On the other hand, since 60% of traffic accidents take place at intersections, it is desired that accidents and hiyari-hattos be detected based on changes in vehicle speed observed by roadside sensors installed at intersections.
To that end, Patent Literature 1, for example, discloses a traffic accident detection apparatus that uses a vehicle detection sensor installed at an intersection. FIG. 1 is a block diagram showing a configuration of traffic accident detection apparatus 10 disclosed in Patent Literature 1. As shown in FIG. 1, traffic accident detection apparatus 10 includes imaging device 11, vehicle detection sensor 12, data recording section 13, data analysis section 14, and recording control section 15.
Imaging device 11 constantly captures the traffic conditions in its observation area. The image data thus captured is temporarily recorded (cached) in data recording section 13. Vehicle detection sensor 12 detects all vehicles within the observation area, monitoring, as well as outputting to data analysis section 14, changes in the position and speed of each vehicle over time.
Data analysis section 14 analyses the data outputted from vehicle detection sensor 12. By way of example, data analysis section 14 determines if an accident or a dangerous situation has occurred by detecting sudden acceleration changes of a vehicle, abnormal proximity of positional data between a plurality of vehicles, and/or the like, and notifies recording control section 15 of the determination result.
If the determination result received from data analysis section 14 indicates that an accident or a dangerous situation has occurred, recording control section 15 has data recording section 13 record the imaged data of a given duration preceding and following that occurrence.
As a filter for correcting errors contained in observation values, the Kalman filter is widely known. As an application example of the Kalman filter, Patent Literature 2, for example, discloses a current position detection apparatus for vehicles which detects the current position of a vehicle based on the vehicle's orientation and traveled distance.
FIG. 2 is a block diagram showing a configuration of current vehicle position detection apparatus 20 disclosed in Patent Literature 2. As shown in FIG. 2, current vehicle position detection apparatus 20 includes vehicle speed sensor 21, gyro 22, GPS 23, relative path computation section 24, absolute position computation section 25, and Kalman filter 26.
By having computations (dead-reckoning computations) carried out at relative path computation section 24 and absolute position computation section 25 based on signals from vehicle speed sensor 21 and gyro 22, vehicle speed, absolute orientation, relative path, and absolute position are outputted. Further, outputs of position, orientation, and vehicle speed are obtained from GPS 23. Based on the vehicle speed, absolute orientation, and absolute position information obtained through dead-reckoning, as well as the vehicle speed, orientation, and position information from GPS 23, Kalman filter 26 performs vehicle speed sensor distance coefficient correction, gyro offset correction, absolute orientation correction, and absolute position correction.